Two dimension autonomous isotropic detection technique

ABSTRACT

The present invention discloses a target detection process that acquires imagery from a target; compares the acquired imagery with image metric data; applies criteria to eliminate false detections and reduce clutter; applies morphological operators on the acquired imagery; probability rank orders the target imagery; divides the imagery into a series of detected target windows; and displays the detected target windows. The imagery undergoes light target detection, dark target detection or both. A morphological operator isolates targets from their background. Two concatenated morphological filter patterns are used to screen imagery data. Spatial discontinuities at the pixel level can be detected. The detected target window images are presented to a user in a mosaic format.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional application of application Ser. No.09/976,040, filed on Oct. 15, 2001, now U.S. Pat. No. 7,298,866, theentire content of which is hereby incorporated by reference.

BACKGROUND OF THE INVENTION

1. Technical Field

The present invention relates to a target or object detection process.

2. Background Information

It is often desired to find a target or object against varioussurrounding backgrounds in very large sampling areas. For example, U.S.Pat. No. 5,848,189, “Method, Apparatus, and System for Verification ofPatterns”, discloses the use of morphological operators to performtemplate matching and pattern verification. U.S. Pat. No. 5,068,910,“Image Processing for Reading Dark and Light Characters from Light andDark Backgrounds Respectively”, discloses reading a dark-coloredcharacter on a light-colored background. U.S. Pat. No. 3,646,264,“Method of Acquiring a Moving Target” discloses a method for acquiring amoving target using the photo-cathode of a scanning tube and looking forsharply defined contrast edges. The disclosures of these patents arehereby incorporated by reference in their entireties.

Known systems involve searching for a target having predeterminedpatterns and characteristics, and involve knowing the specific shapesbeing searched for ahead of time. Searching an area for unknown objectsof unspecified shape has been considered computationally intensive, andassociated with high false alarm rates that adversely impact a specificsearch mission.

SUMMARY

The present invention is directed to a method for detecting an objectimage within image data, the method comprising receiving image data,segmenting the image data into multiple windows, determining alikelihood that each window contains the object and probability rankordering the multiple windows based on the step of determining, andselecting one of the multiple windows as a window wherein the objectimage is considered to reside.

The receiving step can comprise collecting and recording the image dataas the image data emanates back to a receiver. The step of segmentingcan comprise determining a set of image metrics, applying predeterminedselection criteria to filter false detections and clutter from the imagedata, comparing image data, after applying the selection criteria, withthe image metrics, and applying morphological operators on the imagedata.

Exemplary embodiments display at least one of the multiple windows,identify pixels having a lighter contrast compared to other pixels inthe imagery, identify pixels having a darker contrast compared to otherpixels in the imagery, and identify pixels having both lighter anddarker contrast compared to other pixels in the imagery. A morphologicaloperator can be used to isolate targets from their background. Twoconcatenated morphological filters can be applied to filter the imagedata.

The method can detect spatial discontinuities at the pixel level.Detected window images and other image data can be displayed to the userin a mosaic format. The detected window images can also be communicatedto another system. The image data processed comprises, for example,visual data, thermal data, and synthetic aperture radar (SAR) data.

An alternate target detection process according to the inventioncomprises acquiring image data, down-sampling the image data n-times,processing the down-sampled image data for detecting at least one of alight target and a dark target, labeling subsets of the image data thatmay contain target data and rejecting clutter associated with thesesubsets of the image data, combining results of the image data that hasbeen down-sampled, and forwarding combined results to a decision makingauthority. The decision making authority can extract windows and rankorder them. An image can be down-sampled n-times using a series of lowpass filters that can filter in horizontal and\or vertical directions.The process comprises an image that has been down-sampled n-times, wheren comprises a large number that can accomplish target detection afteraccomplishing a larger amount of down-sampling. The filtering processcan be performed by a six by six (6×6) convolution filter. The filteringprocess can also be performed by an N by N convolution filter, where Nis a number greater than or equal to one.

BRIEF DESCRIPTION OF THE INVENTION

Objects and advantages of the invention will be understood by readingthe following detailed description in conjunction with the followingdrawings where:

FIG. 1 illustrates an exemplary process according to the presentinvention;

FIG. 2 illustrates an exemplary Isotropic Detection Technique (IDT)algorithm flow which can be used for segmenting the image data of FIG.1;

FIG. 3A illustrates an exemplary down-sampling of an image n-times;

FIG. 3B shows exemplary filter kernels used in an exemplary convolutionprocess;

FIG. 4 illustrates an exemplary light target detection process;

FIG. 5A illustrates an exemplary erosion filter operation;

FIG. 5B illustrates an exemplary dilation filter operation;

FIG. 6 illustrates an exemplary dark target detection process; and

FIG. 7 illustrates an exemplary region labeling and clutter rejectionprocess.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

FIG. 1 illustrates an exemplary method 100 for detecting an object imagewithin image data according to the present invention. This FIG. 1 methodcomprises receiving image data (e.g. full resolution imagery) in step102. The image data can be received from any source via any knowntechniques including, but not limited to, real time data, delayed datafrom a local source, delayed data from a remote source, or from anydesired source. For example, the present invention can accommodateimages of approximately 1 Gigabyte in size, however the presentinvention can be used with images of any size. The system's ability tohandle the image size will be, in some cases, a function of the hardwareconfiguration used to implement the image segmentation process, such asprocessing capability, memory allocations, and so forth. The FIG. 1method can be implemented on a conventional desktop computer, or anyother computing or processing devices available.

The FIG. 1 method also includes a step of segmenting the image data intomultiple windows. For an exemplary embodiment, the step of segmentingcan be performed by comparing the image data with image metric data. Forexample, image metric data is established in step 101, and can containpredetermined metric data. Image metric data can include parameters suchas the length and width of an image, the ground sample distance of apixel, the dynamic range of data, sensor information, thermal, visual,Synthetic Aperture Radar (SAR) data, etc. Latitude and longitudecoordinates can also be determined by mapping the pixel and linelocations of the target using the types of data that are usuallyincluded in ocean imagery.

The image metric data established in step 101 of FIG. 1 is fed into anisotropic detection technique (IDT) of a segmenting step 103 andcompared with the image data. The image data is segmented by theIsotropic Detection Technique (IDT) in step 103 into multiple windows.The segmenting step 103 can include compressing or expanding the imagedata before, during, and/or after segmentation of the image data intothe multiple windows. Each of the windows includes a subset of the imagedata received in step 102. In step 104, the image data within eachwindow is processed to determine a likelihood that the window containsthe object, and the windows are rank ordered based on the step ofdetermining that is performed by the IDT algorithm and that will bediscussed in greater detail below. One exemplary rank ordering methodcan assign each window a value which corresponds to a probability thatthe window contains image data relating to the object of interest. Thewindows can be rank ordered from highest to lowest, lowest to highest,or any other desired window ranking order.

The Isotropic Detection Technique (IDT) used in an exemplary embodimentis an autonomous, two-dimensional image target detection process. TheIDT works by acquiring and detecting targets that possess contrastdiscontinuity changes along their borders or in their internalstructures. This technique can examine the effect that the target has onthe background surrounding or proximately located next to the target.The IDT algorithm can process large image sets covering upwards ofthousands of square miles. These image sets correspond to hundreds ofmega-pixels. The complete pixel dynamic range or depth is used in thedetection process. In a number of cases, pixel depths up to andincluding 16 bits have been accommodated. The detection portion of theIDT process can automatically determine both the ship's directionalheading and the ship's size.

The segmenting step 103 outputs a series of target windows that in anexemplary embodiment are organized in a target likelihood order in step104. For example, those windows most likely to contain targetinformation can be displayed first, and those with a lesser likelihoodto contain target information are displayed later. Those skilled in theart will recognize that the target information can be displayed in otherways also, such as starting with those target windows least likely tocontain information. The target detection region size, plus somestandard border outline helps to determine the size of the extractedwindows. The border also provides some of the background for context andreference frame. The target windows that are output from the IDTalgorithm and appear first in the rank ordered list are those targetwindows most likely to be a target, while those target windows appearingfurther down the rank ordered list are most likely to be backgroundclutter.

One of the multiple windows is selected as a window wherein the objectimage is considered to reside. The window can be selected automaticallybased on the likelihood determined for the window, or the imagelikelihood determined for the window, or the image data in the multiplewindows can be displayed for the user. For example, the rank orderedtarget windows of step 104 can be presented on a display to the user asa mosaic of the detected images. User review of the information can beenhanced by mosaicing a number of the windows on a single image 106. Asecond image 107 of the total scene down-sampled to fit on the displaycan also be used to provide a synoptic view of the total image, withpossible target information being marked as small boxes 103.1. Forexample, in a maritime application, the possible target information asdisplayed in the second image 107, can be used to provide potentialship-to-ship relationships.

The mosaic of image 106 can be arranged in any manner the user desires.The mosaic of images selected corresponds to one of the multiple windowswhere a object image may reside. This mosaic of images can be arrangedto make the information easy to understand. One possible arrangementformat is to position the target windows from left to right and movingfrom the top row to the bottom row in probability rank order. Thehighest probability window step 104.1 would be in the left-most locationon the first row at the top of the mosaic. The lowest probability windowstep 104.2 would be in the right-most location on the last row locatedat the bottom of the mosaic.

The mosaic format permits an efficient and human factor friendlypresentation to the user. However, in addition to presenting the targetwindow information to a human observer, the target window data can alsobe sent across a communication link to an automated system, computer, orother processing device that is designed to interpret the resultingtarget window data. Further, a threshold value or floor can be set forthe image metric data and only that information that meets this imagemetric criteria, such as size and target orientation, will be forwardedto a user or automated system.

FIG. 2 depicts an IDT Top Level Algorithm flow associated with step 103.Both the image data received in step 100 and the image metric dataestablished in step 101 and are input and processed by the IDT duringthe segmenting step 103.

In step 120, the coordinates of a desired detected target window areobtained and then requests that the corresponding full resolution imagerelating to the target window be pulled.

The image output step 100 is sent to the IDT algorithm in step 103. Herethe image data is down-sampled n-times in step 108 and the output isthen processed for Light Target detection step 110, Dark Targetdetection step 112, or both. One or both of these operational paths canbe run in the algorithm depending upon the type of target, the imagerydata format accumulated and the background. The Light Target detectiondata output from step 110 undergoes a region labeling and clutterprocessing step in step 114. Similarly, the dark target detection datafrom step 112 then undergoes a region labeling and clutter processingstep in step 116. The region that is being labeled is the target regionand clutter is the unwanted and extraneous information that is not partof the target region and needs to be removed so as to not obscure thetarget region.

In 118, the outputs of the Light Target detection step 114 and the DarkTarget detection step 116 are combined into a combined detection viewand the target windows are then pulled based upon this output criteria.A metric is computed in both the Light and Dark Target Detectionroutines.

The step 104 of probability rank ordering the multiple windows uses themetric computed in step 103 to order the target windows to be pulled asshown in step 120. This information along with the location and extentis sent forward to step 120 where the full resolution imagery for thewindow is pulled from the original image data. All the windows areordered in step 104 by their confidence or IDT metric. For example, highconfidence windows are presented first, followed by the lower confidencewindows.

Since most ships are typically brighter than an ocean background,especially in Synthetic Aperture Radar (SAR), most of the imagery can beprocessed with the Light Target detection. However, when a givensituation lends itself more appropriately to Dark Target detection, theimagery can be processed by using Dark Target detection. An exemplaryscenario where Dark Target detection would be effective is the presenceof dark ships in glare or glint imagery in visual or thermal imagery.

FIG. 3A is an exploded view of the down-sampling process. The image isdown-sampled n-times. As shown in 108, the image undergoes a 2:1down-sampling in 108 a, another 2:1 down-sampling in 108 b, and soforth, until the n'th 2:1 down-sampling depicted in 108 n is reached.

The particular selection of the total number of n down-samples isgoverned, at least in part, by the minimum target size to be detected.The down sample inter-pixel distance can, for example, be sized so thatthe minimum object size, such as a ship and all wakes, is approximately1½ to 2 pixels in the maximum direction after down-sampling, althoughthis sizing can be adjusted to achieve different performance levels.

The down sampling process can serve as a clutter rejecter. However, thelevel of down-sampling to reject clutter can be balanced against toomuch down-sampling that results in the loss of small objects. Anotherbenefit of down sampling the original imagery is that down-sampling stepcan reduce the amount of pixel processing that is used in the algorithmsuite. The down-sampling of pixels is not computationally expensive.Therefore, when down-sampling is combined with the rest of the algorithmsuite, the resulting total pixel computation load factor is extremelylow. As mentioned earlier, IDT simulation runs quickly on a computer,such as a typical desk top computer.

A further detailed view of the 2:1 down-sampling of 108 a is shown inFIG. 3.A. Step 108.1 shows that the low pass filtering of the image datais performed in the X direction. Step 108.2 shows the down-samplingperformed in the X direction. Step 108.3 shows the low pass filtering ofthe image data performed in the Y direction and step 108.4 shows thedown-sampling in the Y direction.

Each time that a 2:1 down sample is performed, the resulting number ofpixels remaining to be processed is reduced by ¼. Therefore, if a totalof three down-samples are performed, the resulting image would be 1/256as large as the original. Consequently, a 256 mega-pixel image would bereduced to a 1 mega-pixel image for processing in the rest of thealgorithm.

FIG. 3A shows an image down sampler. The down-sampling can be done asmany times as desired to achieve a desired reduced resolution image. Theimage can be first processed by applying a low pass filter, and followedby decimation in size with a ratio of 2 to 1. One filter constructionthat can be used to remove both aliasing and clutter is a 6×6 low passconvolution filter. This filter can remove the high frequencies that cancause aliasing when the image is resampled by the down-sampler. A columnfilter can then be used to operate on every other column element. Thiswill result in a reduction of ¼ the total number of operations.

Although target reduction to one or two pixels can be performed, higheror lower levels of target reduction can be implemented, depending uponthe specific application. For example, if a ship having a length of 100meters is to be detected and the image resolution is about 15 meters perpixel, then two to three 2:1 down-samples, or any other number and/orratio of down-samples, are performed.

As shown in FIG. 3A, the filter down-sample combination is repeated asdesired to achieve the desired resolution so that a target is, forexample, one to two pixels in extent. For example, to detect a ship of100 meters in length, where the image resolution is about 15 meters, 2to 3, 2:1 down-samples can be performed. Where each a 2:1 down-samplereduces the resulting number of pixels to process by ¼, a total of 3down-samples will render the resulting image 1/256 as large as theoriginal (e.g., a 256 mega-pixel image would be reduced to 1 mega-pixelimage for processing in the rest of the detection algorithm).

FIG. 3B shows filter kernels used in an exemplary convolution process.If a 6×6 kernel is used, it can use 29 adds and 26 shift operations toproduce a new pixel. By decomposing the operation into a single row andsingle column convolution filter, both convolution filters use 11 addsand 8 shift operations. Those skilled in the art will appreciate thatother filters and convolution operators can also be used with thepresent invention. The x direction filter 301 filters in the horizontaldirection, and the y direction filter 302 filters in the verticaldirection. To achieve 2:1 down-sampling in both the horizontal directionfilter 301 and the vertical direction filter 302, a single rowconvolution operator can be configured to work on every other pixel. Thecolumn filter can operate on every other column element of the resultingintermediate image. This will reduce by ¼ the total number ofoperations.

Convolution operators are well known to those skilled in the art. Onefilter that can be used in removing both aliasing artifacts and clutteris a 6×6 low pass convolution filter. This filter can remove highfrequencies that cause aliasing when the image is resampled by thedown-sampler.

Those skilled in the art will appreciate that various convolutionoperators, filter types and sizes can be used in implementing the IDToperator.

The 6×6 convolution array 304 can, in an exemplary embodiment, becalculated in the following manner. Starting with the value “1” in theleft most position of the x direction filter 301 can be multipliedtogether by each value in the y direction filter 302 moving from top tobottom. These values become the first column of the 6×6 array 304.Moving to the next value “4” in the second position of the x directionfilter, each value in the y direction can be multiplied together by eachvalue in the y direction filter 302 moving from top to bottom. Thesevalues become the second column of the 6×6 array. This process isrepeated for each value in the x direction filter to produce the full6×6 array depicted in 304.

FIG. 4 illustrates the Light Target detection process. Both Light Targetdetection Dark Target detection use a similar process. The TargetPrimitive Blocking Filter, in an exemplary embodiment, is made up offour concatenated 3-by-3 morphological filters as shown in FIG. 4. Theimage is processed by the Erosion operator 404.1 and then the Dilationoperator 404.2, using the “T” pattern of Pattern 1. This step removesthe very small target elements. Next, an Erosion operator 404.3 and thena Dilation operator 404.4 using Pattern 2 are applied to the image.Pattern 2 is a slightly bigger pattern and it will remove bigger targetelements. The Erosion and Dilation filter pattern 1 and 2 are shown inFIG. 5A.

Image processing routines are used to define the extent of the targetsand remove objects that are not considered to be targets of interest,based on size or extent. For example, single point targets are usuallyrejected as clutter based on the size and the chance that noise spikeswill be captured and classified as targets. Next, each remaining regionis given a unique label and considered as a candidate target. The extentof each labeled region is determined and a size criteria is applied. Ifan object's dimensions are too large to be a target, it is eliminated asclutter. In other cases, a threshold minimum sizing criterion can beapplied, and candidate targets failing to meet this requirement can beeliminated.

At this stage of the process, objects possessing target like propertieshave been retained and are tested to determine if they have other shipor target like characteristics. For example, the length to widthrelationship of the candidate target can be assessed, along with asimple texture measure across the candidate target.

Because of the flexibility to accommodate Light and Dark Targetdetection and the low false alarm rate, it is possible to run either orboth Light and Dark Target detection algorithms, and optionally combinethe detection results, to achieve desired system performance andaccuracy.

A highest intensity region can be considered to be the most likelytarget. Light Target detection detects candidate targets or elementsthat are lighter than their background. In FIG. 4, a reduced resolutionimage step 402 is sent along two pathways towards a subtractor 406. Thereduced resolution image step 402 is the image that results after thefull resolution imagery input is down-sampled n-times in step 108 ofFIG. 2. A subtractor is a device whose output data is the arithmeticdifference of the two (or more) quantities presented as input data tothe device. One path heading towards the positive input of thesubtractor sends the reduced resolution image through a target primitivefilter step 404 into the positive input side of the subtractor. Anotherpath heading towards the negative input of the subtractor sends thereduced resolution image into the negative input side of the subtractor,in this case bypassing the target primitive filter.

In the FIG. 4 example, the target primitive blocking filter is made upof 4 concatenated 3×3 morphological filters. For ease of description,the image referred to in this stage of the process is the reducedresolution image. The target primitive filter 404 contains fourmorphological filters: two morphological filters for Pattern 1 (404.1and 404.2) and two morphological filters for Pattern 2 (404.3 and404.4). The target primitive blocking filter is used to limit thespatial extent of acceptable targets or target elements. It looks forthe relative brightness of the pixels that make up these targets ortarget elements.

The contents of the Pattern 1 filter erosion/dilation neighborhood areshown in step 410 of FIG. 4 and are known as a “T” pattern. The contentsof the Pattern 2 filter erosion/dilation neighborhood is shown in 412 ofthe same figure and is known as a full 3×3 block pattern. These filterswill remove the candidate target or candidate target elements regionsbased on both size and contrast relative to their background. Thefiltering process suppresses the target regions leaving only backgroundinformation in the output of the filter. This mostly background image isnext subtracted from the reduced resolution image. This process leavesonly the candidate target's regions, candidate target elements, or theperturbations that they cause on the background regions. Perturbationscan include wake, smoke, or motion Doppler shift. The filtering processwill help pickup on these perturbations to help nominate the targetregions.

FIG. 5A shows an exemplary filter operation during the erosion process.In Filter Pattern 1, the input image pixels step 501 undergoes anerosion neighborhood step 502 and results in the Output Image Pixels(prime) step 503, where e′=minimum of {b, d, e, f, h}.

In Filter Pattern 2, the input image pixels step 505 are eroded with theneighborhood step 506 and results in the Output Image Pixels (prime)step 507, where e′=maximum of {a, b, c, d, e, f, g, h, i}. Thereforepixel e′ of the output image is the maximum of the nine pixel values ofthe input image pixels.

FIG. 6 shows the Dark Target detection process. The Dark Targetdetection process can be done separately from the Light Target detectionprocess, since it is looking for dark objects against a lighterbackground. Some examples are visual image ships in a glare or glintsetting, thermally cold ships in a relatively warmer water background.Also, dark regions on the water surface such as oil spills as seen insynthetic aperture radar imagery. As with the Light Target detectionprocess, Pattern Filters 1 and 2 operate on the imagery to remove areasof interest from the scene, followed by a subtraction from theunfiltered image to reveal the targets (ships, oil slicks, etc.)

In FIG. 6, a reduced resolution image is sent along two pathways towardsa subtractor step 606. One path sends the reduced resolution image dataof step 602 towards the negative input of the subtractor step 606.Another path sends the reduced resolution image through a targetprimitive filter step 604 into the positive side of the subtractor.

In FIG. 6, the target primitive filter step 604 contains fourmorphological filters: morphological filters for Pattern 1 (604.1 and604.2) and morphological filters for Pattern 2 (604.3 and 604.4).

The contents of the Pattern 1 filter dilation and erosion neighborhoodare shown in step 612 of FIG. 6. The contents of the Pattern 2 filterdilation and erosion neighborhood are shown in step 614 of the samefigure.

In the Dark Target detection process, the dilation operation isperformed first, followed by the erosion operation. This sequence ofoperations is followed for both Patterns 1 and 2. In the Dark Targetdetection case, the subtraction is performed in the reverse order andthe input image is subtracted from the operated image.

The output from the Dark Target detection module are all the regionswhere candidate dark ships or dark targets (e.g. an oil spill) have beendetected. As with the Light Target detection, the IDT processes willclean up the resulting image, label and rank order the detection. Therank ordering can, for example, be determined by the minimum intensityproduced by the Dark Target detection module of the algorithm, for eachof the label regions that pass all the other processes down thealgorithm chain.

FIG. 5B shows an exemplary filter operation during the dilation process.In Filter Pattern 1, the input image pixels step 508 are operated on inconjunction with the dilation neighborhood step 509 to produce theOutput Image Pixels in step 510. This filter operation focuses on thecenter element “e′”, where e′=maximum of {b, d, e, f, h}. In FIG. 5B,non-primed letters correspond to the input image pixels. In step 510 thefocus is on input image pixel “e” and the rest of the surrounding values“a, b, c, d, f, g, h and i” are its surrounding “neighbor” pixel values.

In Filter Pattern 2, the input image pixels of step 511 are operated onin conjunction with the dilation neighborhood step 512 to produce theOutput Image Pixels in step 513. This filter operation focuses on thecenter element “e′”, where e′=maximum of {a, b, c, d, e, f, g, h, i}.

FIG. 7 is an exemplary illustration of the region labeling and clutterrejection process flow. Image processing routines are used to define theextent of the targets and remove those objects that are not to beconstrued as targets based on their size or extent, or any other desiredcriteria.

These target regions are optionally sent through a series of singlepoint cleaning and clutter rejection routines of step 700. Step 702 is aregion labeler used to label detected candidate regions. Single pointtarget cleaning is accomplished using morphological filters to rejectregions as clutter. Single point targets can be rejected as clutterbased on the size and chance that noise spikes will be captured andclassified as targets. In step 704, minimum and maximum sizing criteriacan be used to reject additional regions as clutter through applicationof sizing criteria with, for example, minimum and/or maximum rejectionthresholds. For example, if a potential target is determined to be toolarge to be an actual target, it is eliminated as clutter. Conversely,if a potential target is determined to be too small to be an actualtarget, it can also be eliminated as clutter. Each remaining region isgiven a unique label and considered a candidate target.

The various features of the image data are computed in step 706. Targetfeatures such as heading, centroid and extent are computed. Furtherclutter rejection is accomplished by the classifier of step 708. As anexample, the classifier can be a simple quadratic statistical classifierthat has been trained to separate targets from clutter. Thisclassification can be based upon any additional constraint requirementsthe user desires for a given application. For example, the classifiercan reject ships having a length greater than, less than, or equal to aspecified length. Targets that are moving in an unwanted heading ordirection can be screened out. Additionally, target length to widthratios, texture, and the IDT metric determined in the Light or DarkTarget detection process can be used, as can any other desired criteria.

The outputs of the process flow shown in FIG. 7 are candidate targetregions coordinated in a full resolution image.

Exemplary IDT systems of the present invention can be used in anyapplication including, but not limited to, maritime applications andterrestrial applications. Exemplary embodiments can process a wide rangeof data types, such as visual, thermal, and Synthetic Aperture Radar(SAR) imagery or any desired data. Exemplary embodiments can detect bothman-made and naturally occurring structures and objects, such as ships,icebergs, ice flows, and oil slicks in a naturally occurring background,such as open ocean, with high detection rates that will, for example,20,000 to 1 data compression ratios (e.g., 90% detection rate, withfalse alarm rate of less than one per 1,000 square nautical miles).Bandwidth compression obtainable is a function of the number of ships inthe viewing area or scene and the false alarm rate. Because excellentdata compression ratios can be achieved, it is practical for a person toreview and digest the data, or the image data can be disseminated overlow bandwidth communication networks.

The present invention has been described with reference to preferredembodiments. However, it will be readily apparent to those skilled inthe art that it is possible to embody the invention in specific formsother than that described above, and that this may be done withoutdeparting from the spirit of the invention. The preferred embodimentsare merely illustrative and should not be considered restrictive in anyway. The scope of the invention is given by the appended claims, ratherthan the preceding description, and all variations and equivalents whichfall within the range of the claims are intended to be embraced therein.

1. A target detection process comprising: acquiring image data at aprocessor; down-sampling the image data in the processor n-times;processing the down-sampled image data for detecting at least one of alight target and a dark target; labeling subsets of the image data thatmay contain target data and rejecting clutter associated with thesesubsets of the image data; combining results of the image data that hasbeen down-sampled; and extracting and rank ordering plural windows basedon the combined results.
 2. The process of claim 1, wherein theextracting and rank ordering step is performed in a decision makingauthority of the processor.
 3. The process of claim 1, comprising: animage that is down-sampled n-times using a series of low pass filtersthat can filter in a horizontal and vertical direction.
 4. The processof claim 1, comprising: an image that has been down-sampled n-times,where n comprises a large number that can still accomplish targetdetection after accomplishing a larger amount of down-sampling.
 5. Theprocess of claim 1, wherein down sampling the image data comprises:filtering the acquired image data through a six by six (6×6) convolutionfilter.
 6. The process of claim 1, wherein down-sampling the image datacomprises: filtering the acquired image data through an N by Nconvolution filter, where N is a number greater than or equal to one. 7.The process of claim 1, wherein the processor is performed using anisotropic detector.